This function generate temperature predictions (in 10^6/T2) based on a calibration dataset and target D47. Note that this approach additionally accounts for measured error in the target D47. This approach is congruent with the one used in McClelland et al. (2022).

This function generate temperature predictions (in 10^6/T2) based on a calibration dataset and target D47. Note that this approach additionally accounts for measured error in the target D47. This approach is congruent with the one used in McClelland et al. (2022).

Usage

rec.bayesian(
  calModel,
  recData,
  iter = 1000,
  mixed = FALSE,
  postcalsamples = NULL,
  MC = TRUE
)

Arguments

calModel

The stan model to be analyzed.

recData

The reconstruction dataset.

iter

Number of replicates to retain.

mixed

whether the model calModel is mixed or not.

postcalsamples

Number of posterior samples to analyze from the calibration step.

MC

Multicore (TRUE/FALSE)